1. Field of the Invention
The present invention generally relates to a computer-based method for business management and, more particularly, to a computer implemented method for modeling intrafirm interactions between divisions within a firm operating in a centralized mode.
2. Description of the Related Art
The terminology used for this description is in general accordance with the accepted terminology used by persons of ordinary skill in the relevant arts. Addition definitions will be given where appropriate. For example, the term "marketing" is defined herein as the process of planning and executing the conception, pricing, promotion and distribution of ideas, goods and services to create exchanges that satisfy individual and organizational objectives, which is in accordance with the well-known treatise Kotler, P., Marketing Management, 7.sup.th Edition, Prentice-Hall, Englewood Cliffs N.Y., 1991. Operations management is defined herein as the management of the direct resources required to produce the goods and services provided by an organization. Finance is defined as the function that manages cash levels in the firm and enables it to make expenditures on promotions, purchase of raw materials, etc.
Researchers in the area of marketing have developed various models for causal forecasting of demands. A subset of these causal models is employed by marketing managers to assist their devising of business strategies. One such causal model is the model of consumer choice, based largely on household panel data, which has enabled researchers to study choice behavior, brand preferences, and purchase habits. Consumer choice models have a bearing on market share models, which have also been studied extensively. Market share models are viewed as useful for evaluating the competitive effects of price and promotions on market shares of each brand and involve using aggregate data at store, regional, or market level.
Example studies of the dynamic interface between marketing and operations are found in: Welam, U. P., On a Simultaneous Decision Model for Marketing, Production, and Finance, Management Science, 23, 9, 1977, 1005-1009; Eliashberg, J., and R. Steinberg, Marketing-Production Decisions in an Industrial Channel of Distribution, Management Science, 33, 8, 1987, 981-1000; Porteus, E., and S. Whang, On Manufacturing/Marketing Incentives, Management Science, 37, 9, 1991, 1166-1181; Rajan, A., Rakesh, and R. Steinberg, Dynamic Pricing and Ordering Decisions by a Monopolist, Management Science, 38, 2, 1992, 240-262; and Sogomonian, A. G., and C. S. Tang, A Modeling Framework for Coordinating Promotion and Production Decisions within a Firm, Management Science, 39, 2, 1993, 191-203.
Much of the above-identified prior work, however, has studied the dynamics of the marketing-operations interface using only one product with deterministic demands. For example, the above-cited work by Porteus and Whang has considered a single-time-period model with multiple end-products. The cited Porteus and Whang model focuses on developing appropriate incentives to make the efforts of "selfish" marketing and operations managers result in a global optimal. Neither that model nor the other above-cited models, however, focus on interactions between different brands and the effect of competition.
The model in the above-cited work of Welam, U. P., relates to a limited study of simultaneous decision making in marketing, finance, and operations. Major limitations of the Welam model are that it only focuses on one product and does not consider the impact of competition between brands.
Marketing forecasts of product consumption and predictions of the success of impending or candidate marketing strategies to sell a product are extremely important to operational management, as such forecasts would allow management to plan and evaluate product production schedules which correspond to, and are matched with, marketing forecasts and predictions reflecting those candidate marketing strategies. However, marketing is an extremely dynamic field and, therefore, a good market model for a particular product category must consider a wide range of variables to ensure the best model possible. None of the known studies or research undertaken to date have sufficiently brought together or proposed an integrated system which allows for inter-firm cooperation/decision-making between marketing, operational management, and finance using a complete market model, geared toward product production, which allows for multiple competitors, marketing strategies, anticipated customer consumption, interaction with like product brands, and overall market health.
Previous researchers have identified market models combining some features of what is termed as a micro-level analysis, which is based on direct survey-type consumer choice data, with what is termed as a macro-level analysis, which is based on aggregate data-based market share information.
One example is Russell, G. J., and W. A. Kamakura, Understanding Brand Competition using Micro and Macro Level Scanner Data, Journal of Marketing Research, 31, 1994, 289-303, (the Russell, et al., Understanding Brand Competition model). There are, however, shortcomings in this method. One is that it does not consider or model linkage between marketing management, manufacturing/inventory operations, and finance division of the firm. Another shortcoming, which will be understood to one of ordinary skill from the description of the present invention below, is that the Russell, et al., Understanding Brand Competition Model does not allow its explanatory or marketing mix variables to be selected as Multi-nominal Logit (MNL) or Multiplicative Competitive Interaction (MCI) variables. Instead, the Russell et al. model sets all of its explanatory or marketing mix variables as MNL variables.
Another example of a market model which uses micro and macro level scanner data is termed herein as the "Garg market model", and is described in related U.S. patent application Ser. No. 09/032,527, Integrated Marketing and Operations Decision-making Under Multi-brand Competition, filed Feb. 27, 1998. The Garg market model is more comprehensive than those discussed above in that it addresses the issue of integrated decision-making under multi-brand competition when the firm consists of two divisions: marketing and operations, operating in a decentralized mode. Also, the Garg model allows the marketing mix variables to be selected as either Multi-nominal Logit (MNL) or Multiplicative Competitive Interaction (MCI) variables. However, the cited Garg model does not consider a firm comprising: Marketing Management, Operations Management, and Finance Management.
Therefore, there is a need for an integrated system for coordinated decision making between the Marketing, Operations and Finance branches of a firm competing under multi-brand competition.